One further issue foregrounded in deep RL involves the role of inductive biases in shaping representation learning. Most deep RL systems that take visual inputs employ a processing architecture [1]} that biases them toward representations that take into account the translational invariance of images. And more recently developed architectures build in a bias to represent visual inputs as comprising sets of discrete objects with recurring pairwise relationships [2]}, [3]}. Such ideas recall existing neuroscientific findings [4]}, and have interesting consequences in deep RL, such as the possibility of exploring and learning much more efficiently by decomposing the environment into objects [5]}, [2]}.
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